Application of computer simulation results and machine learning in analysis of microwave radiothermometry data

TL;DR

This paper explores the use of machine learning algorithms to analyze microwave radiothermometry data for early breast cancer diagnosis, focusing on modeling temperature distributions and improving diagnostic accuracy.

Contribution

It introduces a novel application of various machine learning algorithms to analyze microwave radiothermometry data for breast cancer detection.

Findings

Machine learning algorithms effectively model temperature fields.

Support vector machine and gradient boosting show high accuracy.

The approach enhances early diagnosis capabilities.

Abstract

This work was done with the aim of developing the fundamental breast cancer early differential diagnosis foundations based on modeling the space-time temperature distribution using the microwave radiothermometry method and obtained data intelligent analysis. The article deals with the machine learning application in the microwave radiothermometry data analysis. The problems associated with the construction mammary glands temperature fields computer models for patients with various diagnostics classes, are also discussed. With the help of a computer experiment, based on the machine learning algorithms set (logistic regression, naive Bayesian classifier, support vector machine, decision tree, gradient boosting, K-nearest neighbors, etc.) usage, the mammary glands temperature fields computer models set adequacy.